


from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any







@dataclass
class 							lowerCamelCase__						:






			"""simple docstring"""






			_UpperCamelCase		:       int
			_UpperCamelCase		:       Node | None 			=		None
			_UpperCamelCase		:       Node | None 			=		None



def 							UpperCamelCase_(					)->		Node | None:
			UpperCamelCase__       =							Node(1	)
			UpperCamelCase__       =							Node(2	)
			UpperCamelCase__       =							Node(3	)
			UpperCamelCase__       =							Node(4	)
			UpperCamelCase__       =							Node(5	)
			return tree
def 							UpperCamelCase_(					_A :Node | None	)->		list[int]:
			return [root.data, *preorder(root.left	), *preorder(root.right	)] if root else []
def 							UpperCamelCase_(					_A :Node | None	)->		list[int]:
			return postorder(root.left	) + postorder(root.right	) + [root.data] if root else []
def 							UpperCamelCase_(					_A :Node | None	)->		list[int]:
			return [*inorder(root.left	), root.data, *inorder(root.right	)] if root else []
def 							UpperCamelCase_(					_A :Node | None	)->		int:
			return (max(height(root.left	)					,  height(root.right	)	) + 1) if root else 0
def 							UpperCamelCase_(					_A :Node | None	)->		Sequence[Node | None]:
			UpperCamelCase__       =							[]

			if root is None:
						return output

			UpperCamelCase__       =							deque([root]	)

			while process_queue:
						UpperCamelCase__       =							process_queue.popleft()
						output.append(node.data	)

						if node.left:
									process_queue.append(node.left	)
						if node.right:
									process_queue.append(node.right	)
			return output
def 							UpperCamelCase_(					_A :Node | None					,  _A :int	)->		Sequence[Node | None]:
			UpperCamelCase__       =							[]

			def populate_output(_A :Node | None					,  _A :int	) -> None:
						if not root:
									return
						if level == 1:
									output.append(root.data	)
						elif level > 1:
									populate_output(root.left					,  level - 1	)
									populate_output(root.right					,  level - 1	)

			populate_output(_A					,  _A	)
			return output
def 							UpperCamelCase_(					_A :Node | None					,  _A :int	)->		Sequence[Node | None]:
			UpperCamelCase__       =							[]

			def populate_output(_A :Node | None					,  _A :int	) -> None:
						if root is None:
									return
						if level == 1:
									output.append(root.data	)
						elif level > 1:
									populate_output(root.right					,  level - 1	)
									populate_output(root.left					,  level - 1	)

			populate_output(_A					,  _A	)
			return output
def 							UpperCamelCase_(					_A :Node | None	)->		Sequence[Node | None] | list[Any]:
			if root is None:
						return []

			UpperCamelCase__       =							[]

			UpperCamelCase__       =							0
			UpperCamelCase__       =							height(_A	)

			for h in range(1					,  height_tree + 1	):
						if not flag:
									output.append(get_nodes_from_left_to_right(_A					,  _A	)	)
									UpperCamelCase__       =							1
						else:
									output.append(get_nodes_from_right_to_left(_A					,  _A	)	)
									UpperCamelCase__       =							0

			return output
def 							UpperCamelCase_(					)->		None:  # Main function for testing.
			UpperCamelCase__       =							make_tree()
			print(F'''In-order Traversal: {inorder(_A	)}'''	)
			print(F'''Pre-order Traversal: {preorder(_A	)}'''	)
			print(F'''Post-order Traversal: {postorder(_A	)}'''					,  "\n"	)

			print(F'''Height of Tree: {height(_A	)}'''					,  "\n"	)

			print("Complete Level Order Traversal: "	)
			print(level_order(_A	)					,  "\n"	)

			print("Level-wise order Traversal: "	)

			for level in range(1					,  height(_A	) + 1	):
						print(F'''Level {level}:'''					,  get_nodes_from_left_to_right(_A					,  level=_A	)	)

			print("\nZigZag order Traversal: "	)
			print(zigzag(_A	)	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						super().__init__()
						self.register_modules(unet=snake_case   ,    scheduler=snake_case  )







			@torch.no_grad()
			def __call__(				self   ,    snake_case = 1   ,    snake_case = None   ,    snake_case = 50   ,    snake_case = "pil"   ,    snake_case = True   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,    generator=snake_case   ,    )
						UpperCamelCase__       =							image.to(self.device  )

						# set step values
						self.scheduler.set_timesteps(snake_case  )

						for t in self.progress_bar(self.scheduler.timesteps  ):
									# 1. predict noise model_output
									UpperCamelCase__       =							self.unet(snake_case   ,    snake_case  ).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									UpperCamelCase__       =							self.scheduler.step(snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						UpperCamelCase__       =							(image / 2 + 0.5).clamp(0   ,    1  )
						UpperCamelCase__       =							image.cpu().permute(0   ,    2   ,    3   ,    1  ).numpy()
						if output_type == "pil":
									UpperCamelCase__       =							self.numpy_to_pil(snake_case  )

						if not return_dict:
									return (image,), "This is a local test"

						return ImagePipelineOutput(images=snake_case  ), "This is a local test"


